On-Line Workpiece Hardness Monitoring in Stone Machining

Abstract

The application of four types of process signals in the indirect on-line monitoring of stone hardness has been analysed in this paper. Cutting forces, servomotor currents, vibration and acoustic emission signals were measured during the drilling of three types of stones characterised by different hardness and heterogeneity values. A group of features were extracted from each signal from the time and frequency domain. Their capacity to correctly classify stone hardness was analysed using an artificial neural network classifier. Stone samples were drilled with new drill bits and drill bits worn to three different wear levels in order to analyse the influence of tool wear on the hardness classification process. Nine combinations of cutting parameters were applied for each drill wear level and stone type. Features extracted from the vibration signals obtained the best results in the stone hardness classification. The results indicate their potential industrial application, since they have achieved a high classification precision regardless of the drill bit wear level.

The Influence of Internally Cooled Drill Bits on Cutting Dynamics and Workpiece Hardness Monitoring in Stone Machining

“…Subsequent to the measurements, all signals were filtered using a Butterworth lowpass filter with a cut-off frequency of 2 Hz. As in previous studies [11] and in the present one (see Section 3), it was observed that the force components F X and F Y were significantly smaller compared to the vertical component F Z . This was in accordance with the vertical tool movement, which was characteristic of the utilized drilling process.…”

“…This effect has been confirmed in numerous studies related to different types of workpiece materials, machining processes, and parameters [8][9][10]. On the other hand, vibration signals have exhibited promising results in the real-time monitoring of stone hardness during CNC drilling with conventional drilling bits [11]. Hence, it seems interesting to analyze whether vibration signals can also be employed for the monitoring of stone hardness in scenarios involving reduced machine vibrations.…”

“…The feature extraction and analysis method employed in this study was initially developed for tool wear monitoring, as described in detail in [21]. Subsequently, its effectiveness was demonstrated in the context of stone hardness classification during drilling with a conventional industrial drill bit without cooling capabilities, as discussed in [11]. The process starts with converting the raw sampled vibration signals from the time domain to the frequency domain through the Fast Fourier Transformation (FFT).…”

“…However, the low tool wear intensity observed in this study, caused by the utilization of the cooled drill bit, should not impact the overall conclusion about the utility of vibration signals in the classification of stone hardness. According to [11], features extracted from vibration signals achieved high classification precision in the case of conventional industrial drill bits, regardless of the degree of tool wear.…”

“…Unlike many commercially available hardness testers, this device was constructed as a self-contained instrument that incorporated a vision system for the selection of the measurement area based on stone texture, an indentation feed drive, and a second vision system for conducting the actual indentation measurements. The measurements were conducted following the Knoop hardness test (EN 14205:2003) and are more comprehensively detailed in [11]. Two variants of drill bits were employed in the experiments.…”